Links for Keyword: Brain imaging

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To develop better nervous-system visualizations, we need to think BIG

By Tyler Sloan If I ask you to picture a group of “neurons firing,” what comes to mind? For most people, it’s a few isolated neurons flashing in synchrony. This type of minimalist representation of neurons is common within neuroscience, inspired in part by Santiago Ramón y Cajal’s elegant depictions of the nervous system. His work left a deep mark on our intuitions, but the method he used—Golgi staining—highlights just 1 to 5 percent of neurons. More than a century later, researchers have mapped out brain connectivity in such detail that it easily becomes overwhelming; I vividly recall an undergraduate neurophysiology lecture in which the professor showed a wiring diagram of the primary visual cortex to make the point that it was too complex to understand. We’ve reached a point where simple wiring diagrams no longer suffice to represent what we’re learning about the brain. Advances in experimental and computational neuroscience techniques have made it possible to map brains in more detail than ever before. The wiring diagram for the whole fly brain, for example, mapped at single-synapse resolution, comprises 2.7 million cell-to-cell connections and roughly 150 million synapses. Building an intuitive understanding of this type of complexity will require new tools for representing neural connectivity in a way that is both meaningful and compact. To do this, we will have to embrace the elaborate and move beyond the single neuron to a more “maximalist” approach to visualizing the nervous system. I spent my Ph.D. studying the spinal cord, where commissural growth cones are depicted as pioneers on a railhead extending through uncharted territory. The watershed moment for me was seeing a scanning electron micrograph of the developing spinal cord for the first time and suddenly understanding the growth cone’s dense environment—its path was more like squeezing through a crowded concert than wandering across an empty field. I realized how poor my own intuitions were, which nudged me toward learning the art of 3D visualization. © 2024 Simons Foundation

Big Brother of the Brain Is Here

By Adolfo Plasencia Recently, a group of Australian researchers demonstrated a “mind-reading” system called BrainGPT. The system can, according to its creators, convert thoughts (recorded with a non-invasive electrode helmet) into words that are displayed on a screen. Essentially, BrainGPT connects a multitasking EEG encoder to a large language model capable of decoding coherent and readable sentences from EEG signals. Is the mind, the last frontier of privacy, still a safe place to think one’s thoughts? I spoke with Harvard-based behavioral neurologist Alvaro Pascual-Leone, a leader in the study of neuroplasticity and noninvasive brain stimulation, about what it means and how we can protect ourselves. The reality is that the ability to read the brain and influence activity is already here. It’s no longer only in the realm of science fiction. Now, the question is, what exactly can we access and manipulate in the brain? Consider this example: If I instruct you to move a hand, I can tell if you are preparing to move, say, your right hand. I can even administer a precise “nudge” to your brain and make you move your right hand faster. And you would then claim, and fully believe, that you moved it yourself. However, I know that, in fact, it was me who moved it for you. I can even force you to move your left hand—which you were not going to move—and lead you to rationalize why you changed your mind when in fact, our intervention led to that action you perceive as your choice. We have done this experiment in our laboratory. In humans, we can modify brain activity by reading and writing in the brain, so to speak, though we can affect only very simple things right now. In animals, we can do much more complex things because we have much more precise control of the neurons and their timing. But the capacity for that modulation of smaller circuits progressively down to individual neurons in humans is going to come, including much more selective modification with optogenetic alternatives—that is, using light to control the activity of neurons. © 2024 NautilusNext Inc.,

Spinal cord ‘atlas’ offers unprecedented insights into injuries

By Miryam Naddaf Researchers have developed a four-dimensional model of spinal-cord injury in mice, which shows how nearly half a million cells in the spinal cord respond over time to injuries of varying severity. The model, known as a cell atlas, could help researchers to resolve outstanding questions and develop new treatments for people with spinal-cord injury (SCI). “If you know what every single cell on the spinal cord is doing in response to injury, you could use that knowledge to develop tailor-made and mechanism-based therapies,” says Mark Anderson, a neurobiologist at the Swiss Federal Institute of Technology in Geneva, Switzerland, who worked on the atlas. “Things don’t need to be a shot in the dark.” Anderson and his colleagues used machine-learning algorithms to build the atlas by mapping data from RNA sequencing and other cell-biology techniques. They described the work in a Nature paper published today1 and have made the entire atlas available through an online platform. The atlas is a valuable resource for testing hypotheses about SCI, says Binhai Zheng, who studies spinal-cord regeneration at the University of California, San Diego. “There are a lot of hidden treasures.” The researchers examined sections of the spinal cord, sampled from 52 injured and uninjured mice at 1, 4, 7, 14, 30 and 60 days after injury. Their analysis involved 18 experimental SCI conditions, including different types of injury and levels of severity. They used RNA-sequencing tools to explore how 482,825 cells responded to injury over time. © 2024 Springer Nature Limited

Related chapters from BN: Chapter 11: Motor Control and Plasticity; Chapter 2: Functional Neuroanatomy: The Cells and Structure of the Nervous System
Related chapters from MM:Chapter 5: The Sensorimotor System; Chapter 2: Neurophysiology: The Generation, Transmission, and Integration of Neural Signals
Link ID: 29368 - Posted: 06.26.2024

This new brain-mapping device could make neurosurgery safer

Jon Hamilton A flexible film bristling with tiny sensors could make surgery safer for patients with a brain tumor or severe epilepsy. The experimental film, which looks like Saran wrap, rests on the brain’s surface and detects the electrical activity of nerve cells below. It’s designed to help surgeons remove diseased tissue while preserving important functions like language and memory. “This will enable us to do a better job,” says Dr. Ahmed Raslan, a neurosurgeon at Oregon Health and Science University who helped develop the film. The technology is similar in concept to sensor grids already used in brain surgery. But the resolution is 100 times higher, says Shadi Dayeh, an engineer at the University of California, San Diego, who is leading the development effort. In addition to aiding surgery, the film should offer researchers a much clearer view of the neural activity responsible for functions including movement, speech, sensation, and even thought. “We have these complex circuits in our brains,” says John Ngai, who directs the BRAIN Initiative at the National Institutes of Health, which has funded much of the film’s development. “This will give us a better understanding of how they work.” Mapping an ailing brain The film is intended to improve a process called functional brain mapping, which is often used when a person needs surgery to remove a brain tumor or tissue causing severe epileptic seizures. © 2024 npr

Redrawing Santiago Ramón y Cajal: Q&A with Dawn Hunter

By Rebecca Horne The drawings and photographs of Santiago Ramón y Cajal are familiar to any neuroscientist—and probably anyone even remotely interested in the field. Most people who take a cursory look at his iconic images might assume that he created them using only direct observation. But that’s not the case, according to a paper published in March 2024 by Dawn Hunter, visual artist and associate professor of art at the University of South Carolina, and her colleagues. For instance, the Golgi-stained tissue Ramón y Cajal drew contained neurons that were cut in half—so he painstakingly reconstructed the cells by drawing from elements in multiple slides. And he also fleshed out his illustrations using educated guesses and classical drawing principles, such as contrast and occlusion. In this way, Ramón y Cajal’s art training was essential to his research, Hunter says. She came across Ramón y Cajal’s drawings while creating illustrations for a neuroscience textbook. “The first time I saw his work, out of pure inspiration, I decided to draw it,” she says. “It was in those moments of drawing that I realized his process was more profound and conceptually layered than merely retracing pencil lines with ink. Examining Ramón y Cajal’s work through the act of drawing is a more active experience than viewing his work as a gallery visitor or in a textbook.” In 2015, Hunter installed her drawings and paintings alongside original Ramón y Cajal works in an ongoing exhibition at the U.S. National Institutes of Health (NIH). That effort led to a Fulbright fellowship to Spain in 2017, providing her access to the Legado Cajal archives at the Instituto Cajal National Archives, which contain thousands of Ramón y Cajal artifacts. Hunter spoke to The Transmitter about her research in Spain and her realizations about how Ramón y Cajal worked as an artist and as a scientist. The Transmitter: What do you think your work contributes that is new? Dawn Hunter: It spells out the connection to [Ramón y Cajal’s] art training. There are some things that to me as a painter are obvious to zero in on that nobody’s really talked about. For example, Ramón y Cajal’s copying of the Renaissance painter Rafael’s entire portfolio. That in itself is a profound thing. © 2024 Simons Foundation

Biological puzzles abound in an up-close look at a human brain

By Laura Sanders It’s a bit like seeing a world in a grain of sand. Except the view, in this case, is the exquisite detail inside a bit of human brain about half the size of a grain of rice. Held in that minuscule object is a complex collective of cells, blood vessels, intricate patterns and biological puzzles. Scientists had hints of these mysteries in earlier peeks at this bit of brain (SN: 6/29/21). But now, those details have been brought into new focus by mapping the full landscape of some 57,000 cells, 150 million synapses and their accompanying 23 centimeters of blood vessels, researchers report in the May 10 Science. The full results, the scientists hope, may lead to greater insights into how the human brain works. “We’re going in and looking at every individual connection attached to every cell — a very high level of detail,” says Viren Jain, a computational neuroscientist at Google Research in Mountain View, Calif. The big-picture goal of brain mapping efforts, he says, is “to understand how human brains work and what goes wrong in various kinds of brain diseases.” The newly mapped brain sample was removed during a woman’s surgery for epilepsy, so that doctors could reach a deeper part of the brain. The bit, donated with the woman’s consent, was from the temporal lobe of the cortex, the outer part of the brain involved in complex mental feats like thinking, remembering and perceiving. This digital drawing of a person's head shows the brain inside. An arrow points to the bottom left side of the brain. After being fixed in a preservative, the brain bit was sliced into almost impossibly thin wisps, and then each slice was imaged with a high-powered microscope. Once these views were collected, researchers used computers to digitally reconstruct the three-dimensional objects embedded in the piece of brain. © Society for Science & the Public 2000–2024

Cubic millimetre of brain mapped in spectacular detail

By Carissa Wong Researchers have mapped a tiny piece of the human brain in astonishing detail. The resulting cell atlas, which was described today in Science1 and is available online, reveals new patterns of connections between brain cells called neurons, as well as cells that wrap around themselves to form knots, and pairs of neurons that are almost mirror images of each other. The 3D map covers a volume of about one cubic millimetre, one-millionth of a whole brain, and contains roughly 57,000 cells and 150 million synapses — the connections between neurons. It incorporates a colossal 1.4 petabytes of data. “It’s a little bit humbling,” says Viren Jain, a neuroscientist at Google in Mountain View, California, and a co-author of the paper. “How are we ever going to really come to terms with all this complexity?” The brain fragment was taken from a 45-year-old woman when she underwent surgery to treat her epilepsy. It came from the cortex, a part of the brain involved in learning, problem-solving and processing sensory signals. The sample was immersed in preservatives and stained with heavy metals to make the cells easier to see. Neuroscientist Jeff Lichtman at Harvard University in Cambridge, Massachusetts, and his colleagues then cut the sample into around 5,000 slices — each just 34 nanometres thick — that could be imaged using electron microscopes. Jain’s team then built artificial-intelligence models that were able to stitch the microscope images together to reconstruct the whole sample in 3D. “I remember this moment, going into the map and looking at one individual synapse from this woman’s brain, and then zooming out into these other millions of pixels,” says Jain. “It felt sort of spiritual.” When examining the model in detail, the researchers discovered unconventional neurons, including some that made up to 50 connections with each other. “In general, you would find a couple of connections at most between two neurons,” says Jain. Elsewhere, the model showed neurons with tendrils that formed knots around themselves. “Nobody had seen anything like this before,” Jain adds. © 2024 Springer Nature Limited

Brain-reading device is best yet at decoding ‘internal speech’

By Miryam Naddaf Scientists have developed brain implants that can decode internal speech — identifying words that two people spoke in their minds without moving their lips or making a sound. Although the technology is at an early stage — it was shown to work with only a handful of words, and not phrases or sentences — it could have clinical applications in future. Similar brain–computer interface (BCI) devices, which translate signals in the brain into text, have reached speeds of 62–78 words per minute for some people. But these technologies were trained to interpret speech that is at least partly vocalized or mimed. The latest study — published in Nature Human Behaviour on 13 May1 — is the first to decode words spoken entirely internally, by recording signals from individual neurons in the brain in real time. “It's probably the most advanced study so far on decoding imagined speech,” says Silvia Marchesotti, a neuroengineer at the University of Geneva, Switzerland. “This technology would be particularly useful for people that have no means of movement any more,” says study co-author Sarah Wandelt, a neural engineer who was at the California Institute of Technology in Pasadena at the time the research was done. “For instance, we can think about a condition like locked-in syndrome.” The researchers implanted arrays of tiny electrodes in the brains of two people with spinal-cord injuries. They placed the devices in the supramarginal gyrus (SMG), a region of the brain that had not been previously explored in speech-decoding BCIs. © 2024 Springer Nature Limited

Related chapters from BN: Chapter 2: Functional Neuroanatomy: The Cells and Structure of the Nervous System; Chapter 19: Language and Lateralization
Related chapters from MM:Chapter 2: Neurophysiology: The Generation, Transmission, and Integration of Neural Signals; Chapter 15: Language and Lateralization
Link ID: 29302 - Posted: 05.14.2024

New genetic tools usher amphibian neuroscience research into modern age

By Angie Voyles Askham The ability of amphibians to metamorphosize and, in some cases, regenerate limbs and even brain tissue raises puzzling yet fundamental questions about how a nervous system wires itself up. For example, if a frog’s legs don’t exist when its brain begins to develop—those limbs later replace its tadpole tail—how are the neural connections maintained such that, once the legs take shape, a frog can move them? “How many connections are there between the spinal cord and the brain? How do they change over metamorphosis?” asks Lora Sweeney, assistant professor at the Institute of Science and Technology Austria. To find out, Sweeney and her colleagues decided to screen a panel of adeno-associated viruses (AAVs) in two species of frog and a newt. These viruses are commonly used to genetically manipulate brain cells in rodents and monkeys, but they have not been proven useful in amphibian experiments. With the right techniques, most common AAVs can deliver genes to amphibian cells through a process called transduction, according to Sweeney’s unpublished results, though the most effective viruses vary by species. These amphibian-friendly AAVs can be used to trace neuronal connections and track groups of neurons born at the same time, the new work shows. And a subset of these same AAVs can also transduce cells in axolotls, newts’ fuzzy-gilled Mexican cousins, according to another preprint from an independent team. Both preprints were posted on bioRxiv in February. “It’s a big game-changer,” says Helen Willsey, assistant professor of psychiatry at the University of California, San Francisco, who was not involved in either study but works with amphibian models. “It opens up a lot of doors for new experiments.” Other researchers had previously tried to get AAVs to transduce cells in frogs and fish, with little success. © 2024 Simons Foundation

Related chapters from BN: Chapter 6: Evolution of the Brain and Behavior; Chapter 2: Functional Neuroanatomy: The Cells and Structure of the Nervous System
Related chapters from MM:Chapter 2: Neurophysiology: The Generation, Transmission, and Integration of Neural Signals
Link ID: 29267 - Posted: 04.24.2024

This fMRI technique promised to transform brain research — why can no one replicate it?

By McKenzie Prillaman It was hailed as a potentially transformative technique for measuring brain activity in animals: direct imaging of neuronal activity (DIANA), held the promise of mapping neuronal activity so fast that neurons could be tracked as they fired. But nearly two years on from the 2022 Science paper1, no one outside the original research group and their collaborators have been able to reproduce the results. Now, two teams have published a record of their replication attempts — and failures. The studies, published on 27 March in Science Advances2,3, suggest that the original results were due to experimental error or data cherry-picking, not neuronal activity after all. But the lead researcher behind the original technique stands by the results. “I’m also very curious as to why other groups fail in reproducing DIANA,” says Jang-Yeon Park, a magnetic resonance imaging (MRI) physicist at Sungkyunkwan University in Suwon, South Korea. Science said in an e-mail to Nature that, although it’s important to report the negative results, the Science Advances studies “do not allow a definitive conclusion” to be drawn about the original work, “because there were methodological differences between the papers”. In conventional functional MRI (fMRI), researchers monitor changes in blood flow to different brain regions to estimate activity. But this response lags by at least one second behind the activity of neurons, which send messages in milliseconds. Park and his co-authors said that DIANA could measure neuronal activity directly, which is an “extraordinary claim”, says Ben Inglis, a physicist at the University of California, Berkeley. © 2024 Springer Nature Limited

Related chapters from BN: Chapter 2: Functional Neuroanatomy: The Cells and Structure of the Nervous System
Related chapters from MM:Chapter 2: Neurophysiology: The Generation, Transmission, and Integration of Neural Signals
Link ID: 29253 - Posted: 04.11.2024

Innovative device records activity from distant brain areas simultaneously

By Claudia López Lloreda As animals carry out complex behaviors, multiple brain areas turn on and talk to one another. But neuroscientists have had limited means to measure that neuronal dialogue. Electrical recordings, for example, are typically constrained to one brain area at a time, or require that mice have their head fixed in a specific position. A new technology overcomes those restrictions. The device, called E-Scope, reported in a peer-reviewed preprint in eLife, effectively measures the activity of neurons in two different areas at the same time, even as rodents move freely. The headset captures images of calcium currents, made using a microscope, and recordings of neurons’ electrical activity through electrodes to show how the cerebellum communicates with other brain regions during social interaction in mice. “Everything [is] synchronized together that way,” says Peyman Golshani, assistant professor of neurology at the University of California, Los Angeles and a study investigator. This approach holds the potential to illuminate how coordination between brain areas in conditions marked by impaired social interaction, such as attention-deficit/hyperactivity disorder and autism, is disrupted, Golshani says. By combining technologies, researchers who use the E-Scope “don’t need separate electrophysiology and imaging hardware,” he adds. It’s also much more comfortable for the animals, according to Golshani. A single wire conveys all of the small headset’s data, so mice can move more freely than when wearing other devices. © 2024 Simons Foundation

Breaking down the winner’s curse: Lessons from brain-wide association studies

By Nico Dosenbach, Scott Marek In 2022, we caused a stir when, together with Brenden Tervo-Clemmens and Damien Fair, we published an article in Nature titled “Reproducible brain-wide association studies require thousands of participants.” The study garnered a lot of attention—press coverage, including in Spectrum, as well as editorials and commentary in journals. In hindsight, the consternation we caused in calling for larger sample sizes makes sense; up to that point, most brain imaging studies of this type were based on samples with fewer than 100 participants, so our findings called for a major change. But it was an eye-opening experience that taught us how difficult it is to convey a nuanced scientific message and to guard against oversimplifications and misunderstandings, even among experts. Being scientific is hard for human brains, but as an adversarial collaboration on a massive scale, science is our only method for collectively separating how we want things to be from how they are. The paper emerged from an analysis of the Adolescent Brain Cognitive Development (ABCD) Study, a large longitudinal brain-imaging project. Starting with data from 2,000 children, Scott showed that an average brain connectivity map he made using half of the large sample replicated almost perfectly in the other half. But when he mapped the association between resting-state activity—a measure of the brain during rest—and intelligence in two matched sets of 1,000 children, he found large differences in the patterns. Even with a sample size of 2,000—large in the human brain imaging world—the brain-behavior maps showed poor reproducibility. For card-carrying statisticians, the result was not surprising. It reflected a pattern known as the winner’s curse, namely that large cross-sectional correlations can occur by chance in small samples. Paradoxically, the largest correlations will be “statistically significant” and therefore most likely to be published, even though they are the most likely to be wrong. © 2024 Simons Foundation

Human brains found at archaeological sites are surprisingly well-preserved

By Nora Bradford Early in her research, forensic anthropologist Alexandra Morton-Hayward came across a paper describing a 2,500-year-old brain preserved in a severed skull. The paper referenced another preserved brain. She found another. And another. By the time she’d reached 12, she noticed all of the papers described the brains as a unique phenomenon. She kept digging. Naturally preserved brains, it turns out, aren’t so rare after all, Morton-Hayward, of the University of Oxford, and colleagues report March 20 in Proceedings of the Royal Society B. The researchers have built an archive of 4,400 human brains preserved in the archaeological record, some dating back nearly 12,000 years. The archive includes brains from North Pole explorers, Inca sacrificial victims and Spanish Civil War soldiers. Because the brains have been described as exceptionally rare, little research has been done on them. “If they’re precious, one-of-a-kind materials, then you don’t want to analyze them or disturb them,” Morton-Hayward says. Less than 1 percent of the archive has been investigated. Matching where the brains were found with historical climate patterns hints at what might keep the brains from decaying. Over a third of the samples persisted because of dehydration; others were frozen or tanned. Depending on the conditions, the brains’ texture could be anywhere from dry and brittle to squishy and tofulike. © Society for Science & the Public 2000–2024.

Mind-reading devices are revealing the brain’s secrets

By Miryam Naddaf Moving a prosthetic arm. Controlling a speaking avatar. Typing at speed. These are all things that people with paralysis have learnt to do using brain–computer interfaces (BCIs) — implanted devices that are powered by thought alone. These devices capture neural activity using dozens to hundreds of electrodes embedded in the brain. A decoder system analyses the signals and translates them into commands. Although the main impetus behind the work is to help restore functions to people with paralysis, the technology also gives researchers a unique way to explore how the human brain is organized, and with greater resolution than most other methods. Scientists have used these opportunities to learn some basic lessons about the brain. Results are overturning assumptions about brain anatomy, for example, revealing that regions often have much fuzzier boundaries and job descriptions than was thought. Such studies are also helping researchers to work out how BCIs themselves affect the brain and, crucially, how to improve the devices. “BCIs in humans have given us a chance to record single-neuron activity for a lot of brain areas that nobody’s ever really been able to do in this way,” says Frank Willett, a neuroscientist at Stanford University in California who is working on a BCI for speech. The devices also allow measurements over much longer time spans than classical tools do, says Edward Chang, a neurosurgeon at the University of California, San Francisco. “BCIs are really pushing the limits, being able to record over not just days, weeks, but months, years at a time,” he says. “So you can study things like learning, you can study things like plasticity, you can learn tasks that require much, much more time to understand.” © 2024 Springer Nature Limited

Related chapters from BN: Chapter 2: Functional Neuroanatomy: The Cells and Structure of the Nervous System; Chapter 11: Motor Control and Plasticity
Related chapters from MM:Chapter 2: Neurophysiology: The Generation, Transmission, and Integration of Neural Signals; Chapter 5: The Sensorimotor System
Link ID: 29159 - Posted: 02.22.2024

The brain is the most complicated object in the universe.

Nicholas J. Kelley In the middle of 2023, a study conducted by the HuthLab at the University of Texas sent shockwaves through the realms of neuroscience and technology. For the first time, the thoughts and impressions of people unable to communicate with the outside world were translated into continuous natural language, using a combination of artificial intelligence (AI) and brain imaging technology. This is the closest science has yet come to reading someone’s mind. While advances in neuroimaging over the past two decades have enabled non-responsive and minimally conscious patients to control a computer cursor with their brain, HuthLab’s research is a significant step closer towards accessing people’s actual thoughts. As Alexander Huth, the neuroscientist who co-led the research, told the New York Times: Combining AI and brain-scanning technology, the team created a non-invasive brain decoder capable of reconstructing continuous natural language among people otherwise unable to communicate with the outside world. The development of such technology – and the parallel development of brain-controlled motor prosthetics that enable paralysed patients to achieve some renewed mobility – holds tremendous prospects for people suffering from neurological diseases including locked-in syndrome and quadriplegia. In the longer term, this could lead to wider public applications such as fitbit-style health monitors for the brain and brain-controlled smartphones. On January 29, Elon Musk announced that his Neuralink tech startup had implanted a chip in a human brain for the first time. He had previously told followers that Neuralink’s first product, Telepathy, would one day allow people to control their phones or computers “just by thinking”. © 2010–2024, The Conversation US, Inc.

To make fMRI more clinically useful, we need to really get BOLD

By Evelyn Lake Functional MRI (fMRI), though expensive, has many properties of an ideal clinical tool. It’s safe and noninvasive. It is widely available in some countries, and increasingly so on a global scale. Its “blood oxygen level dependent,” or BOLD, signal is altered in people with almost any neurological condition and is rich enough to contain information specific to each person, offering the potential for a personalized approach to medical care across a wide spectrum of neurological conditions. But despite enormous interest and investment in fMRI — and its wide use in basic neuroscience research — it still lacks broad clinical utility; it is mainly employed for surgical planning. For fMRI to inform a wider range of clinical decision-making, we need better ways of deciphering what underlying changes in the brain drive changes to the BOLD signal. If someone with Alzheimer’s disease has an increase in functional connectivity (a measure of synchrony between brain regions), for example, does this indicate that synapses are being lost? Or does it suggest that the brain is forming compensatory pathways to help the person avoid further cognitive decline? Or something else entirely? Depending on the answer, one can imagine different courses of treatment. Put simply, we cannot extract sufficient information from fMRI and patient outcomes alone to determine which scenarios are playing out and therefore what we should do when we observe changes in our fMRI readouts. To better understand what fMRI actually shows, we need to use complementary methodologies, such as the emerging optical imaging tool of wide-field fluorescence calcium imaging. Combining modalities presents significant technical challenges but offers the potential for deeper insights: observing the BOLD signal alongside other signals that report more directly on what is occurring in brain tissue. Using these more direct measurements instead of fMRI in clinical practice is not an option — they are unethical to use in people or invasive, requiring physical or optical access to the brain. © 2023 Simons Foundation.

The Chance Discovery that Revolutionized Neuroscience

Kamal Nahas Peter Hegemann, a biophysicist at Humboldt University, has spent his career exploring interactions between proteins and light. Specifically, he studies how photoreceptors detect and respond to light, focusing largely on rhodopsins, a family of membrane photoreceptors in animals, plants, fungi, protists, and prokaryotes.1 Early in his career, his curiosity led him to an unknown rhodopsin in green algae that later proved to have useful applications in neuroscience research. Hegemann became a pioneer in the field of optogenetics, which revolutionized the ways in which scientists draw causal links between neuronal activity and behavior. In the early 1980s during his graduate studies at the Max Planck Institute of Biochemistry, Hegemann spent his days exploring rhodopsins in bacteria and archaea. However, the field was crowded, and he was eager to study a rhodopsin that scientists knew nothing about. Around this time, Kenneth Foster, a biophysicist at Syracuse University, was investigating whether the green algae Chlamydomonas, a photosynthetic unicellular eukaryote related to plants, used a rhodopsin in its eyespot organelle to detect light and trigger the algae to swim. He struggled to pinpoint the protein itself, so he took a roundabout approach and started interfering with nearby molecules that interact with rhodopsins.2 Rhodopsins require the small molecule retinal to function as a photoreceptor. When Foster depleted Chlamydomonas of its own retinal, the algae were unable to use light to direct movement, a behavior that was restored when he introduced retinal analogues. In 1985, Hegemann joined Foster’s group as a postdoctoral researcher to continue this work. “I wanted to find something new,” Hegemann said. “Therefore, I worked on an exotic organism and an exotic topic.” A year later, Hegemann started his own research group at the Max Planck Institute of Biochemistry where he searched for the green algae’s rhodopsin that Foster proposed should exist. © 1986–2024 The Scientist.

Related chapters from BN: Chapter 3: Neurophysiology: The Generation, Transmission, and Integration of Neural Signals; Chapter 6: Evolution of the Brain and Behavior
Related chapters from MM:Chapter 3: The Chemistry of Behavior: Neurotransmitters and Neuropharmacology
Link ID: 29077 - Posted: 01.03.2024

Advances in Mind-Decoding Technologies Raise Hopes (and Worries)

By Fletcher Reveley One afternoon in May 2020, Jerry Tang, a Ph.D. student in computer science at the University of Texas at Austin, sat staring at a cryptic string of words scrawled across his computer screen: “I am not finished yet to start my career at twenty without having gotten my license I never have to pull out and run back to my parents to take me home.” The sentence was jumbled and agrammatical. But to Tang, it represented a remarkable feat: A computer pulling a thought, however disjointed, from a person’s mind. For weeks, ever since the pandemic had shuttered his university and forced his lab work online, Tang had been at home tweaking a semantic decoder — a brain-computer interface, or BCI, that generates text from brain scans. Prior to the university’s closure, study participants had been providing data to train the decoder for months, listening to hours of storytelling podcasts while a functional magnetic resonance imaging (fMRI) machine logged their brain responses. Then, the participants had listened to a new story — one that had not been used to train the algorithm — and those fMRI scans were fed into the decoder, which used GPT1, a predecessor to the ubiquitous AI chatbot ChatGPT, to spit out a text prediction of what it thought the participant had heard. For this snippet, Tang compared it to the original story: “Although I’m twenty-three years old I don’t have my driver’s license yet and I just jumped out right when I needed to and she says well why don’t you come back to my house and I’ll give you a ride.” The decoder was not only capturing the gist of the original, but also producing exact matches of specific words — twenty, license. When Tang shared the results with his adviser, a UT Austin neuroscientist named Alexander Huth who had been working towards building such a decoder for nearly a decade, Huth was floored. “Holy shit,” Huth recalled saying. “This is actually working.”

Related chapters from BN: Chapter 19: Language and Lateralization; Chapter 2: Functional Neuroanatomy: The Cells and Structure of the Nervous System
Related chapters from MM:Chapter 15: Language and Lateralization; Chapter 2: Neurophysiology: The Generation, Transmission, and Integration of Neural Signals
Link ID: 29073 - Posted: 01.03.2024

2023’s Mind-Bending Revelations in the Brain Sciences

By Gary Stix This year was full of roiling debate and speculation about the prospect of machines with superhuman capabilities that might, sooner than expected, leave the human brain in the dust. A growing public awareness of ChatGPT and other so-called large language models (LLMs) dramatically expanded public awareness of artificial intelligence. In tandem, it raised the question of whether the human brain can keep up with the relentless pace of AI advances. The most benevolent answer posits that humans and machines need not be cutthroat competitors. Researchers found one example of potential cooperation by getting AI to probe the infinite complexity of the ancient game of Go—which, like chess, has seen a computer topple the highest-level human players. A study published in March showed how people might learn from machines with such superhuman skills. And understanding ChatGPT’s prodigious abilities offers some inkling as to why an equivalence between the deep neural networks that underlie the famed chatbot and the trillions of connections in the human brain is constantly invoked. Importantly, the machine learning incorporated into AI has not totally distracted mainstream neuroscience from avidly pursuing better insights into what has been called “the most complicated object in the known universe”: the brain. One of the grand challenges in science—understanding the nature of consciousness—received its due in June with the prominent showcasing of experiments that tested the validity of two competing theories, both of which purport to explain the underpinnings of the conscious self. The past 12 months provided lots of examples of impressive advances for you to store in your working memory. Now here’s a closer look at some of the standout mind and brain stories we covered in Scientific American in 2023. © 2023 SCIENTIFIC AMERICAN

8 Stunning New Images From Neuroscience

Emily Baumgaertner This is not a work of art. It’s an image of microscopic blood flow in a rat’s brain, taken with one of many new tools that are yielding higher levels of detail in brain imaging. Here are seven more glorious images from neuroscience research → © 2023 The New York Times Company

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